Impact of the choice of upper limb prosthesis mechanism on kinematics and dynamic quality
Bibliographic record
Abstract
Upper limb prostheses can greatly improve the condition of amputees. However, prosthetic mechanisms have different topologies and there is no consensus on the choice of an appropriate mechanism. This paper evaluates the impact of prosthetic mechanism topology on the prosthesis’ performance during daily tasks. The proposed multibody model is compared to four open-loop and one closed-loop existing mechanisms according to: (1) consumed energy, (2) global and local movement reconstruction errors during inverse kinematics, (3) movement smoothness, which reflects the dynamic appearance of the prosthesis, also called ‘dynamic cosmesis’. Flexion–extension (FE) and pronation–supination (PS) tasks were studied in 15 healthy subjects. All parameters identified at least one group difference (p < 0.0001) in both tasks. Most closed-loop mechanisms (50% in FE and 100 % in PS) including the proposed model were among the most energy-efficient mechanisms. Out of all models, the proposed model was the most energy efficient in FE (2.07 ± 0.69 KJ) and in PS (0.25 ± 0.16 KJ). This model also reproduced the studied movements with the lowest errors (1.39 ± 0.2 mm in FE and 1.38 ± 0.25 mm in PS), especially at the forearm level. The results show that the wrist plays a major role in motion smoothness and that two series mechanisms have exhibited a poor dynamic cosmesis because of their higher jerk cost ((1.73 ± 0.30) × 1010) in FE and (9.29 ± 17) × 1013 in PS tasks)). Finally, the mechanism topology affects the performance of upper limb prostheses and represents a novel aspect in the prostheses design which can be applied to exoskeleton design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".